Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
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Frozen shoulder is a common and debilitating condition characterized by pain and restricted movement at the glenohumeral joint. Various treatment methods have been explored to alleviate symptoms, with suprascapular nerve block (SSNB) emerging as a promising intervention. This meta-analysis aimed to assess the effectiveness of SSNB in treating frozen shoulder. The study protocol was registered with PROSPERO (CRD42023475851). We searched the MEDLINE, Embase, and Cochrane Library databases in November 2023. Randomized controlled trials (RCTs) comparing SSNB against other interventions were included. The primary outcome was any functional patient-reported outcome measure. Secondary outcomes were the visual analogue scale (VAS) for pain, range of motion (ROM), and complications. Risk of bias was assessed using the Cochrane risk of bias v. 2.0 tool.Aims
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